Impact of COVID-19 Pandemic on the Occupation and Mental Health of Students: A Scoping Review
Bibliographic record
Abstract
The COVID-19 pandemic has caused an inevitable impact on the health and well-being of young adults. This study aims to gather the literature on how the pandemic impacted the occupation considering the mental health experiences of anxiety and depression of young adult students. A scoping review with thematic analysis was conducted in three electronic databases OVID Medline, PubMed, and CINAHL and the Journal of Occupational Science. Included articles (a) were in the English language, (b) explained the impact of the COVID-19 pandemic on doing occupations or activities, (c) explained the impact of the COVID-19 pandemic on mental health, including anxiety and/or depression, and (d) have participants who are all or mostly are students aged 18–25 years old. Findings show that the pandemic has disrupted several occupations, such as school, work, sleep, leisure, physical activity, and social participation, leading to adaptation. The studies reviewed indicated a positive and negative correlation between mental health and occupation. Findings imply the benefit for occupational therapists to understand the impact of the pandemic on occupation and mental health. Considering the findings, more peer-reviewed and rigorous studies are needed to evaluate further how the pandemic influences individuals’ overall health and well-being. This will prepare society to overcome future health predicaments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".